Verification apparatus, verification method and program

ABSTRACT

A verification apparatus includes: a detection section that detects some or all of junction points, endpoints and turning points of a physical trait of a body part on an input image as feature points, the physical trait being used for verification; and a search section that searches a registered image of the physical trait for a pattern that is the same as or similar to a pattern of the feature points in a center area of the image whose vertical center line is perpendicular to the direction of motion of the physical trait that horizontally moves on a surface on which the body part is put, the registered image being taken along a curved surface of the body part and the center area being between two lines each of which is a predetermined distance away from the center line in opposite directions.

CROSS REFERENCES TO RELATED APPLICATIONS

The present invention contains subject matter related to Japanese PatentApplication JP2006-339059 filed in the Japanese Patent Office on Dec.15, 2006, the entire contents of which being incorporated herein byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a verification apparatus, verificationmethod and program, and is preferably applied to biometric verification,for example.

2. Description of the Related Art

Biometric verification is used to identify a user based on his/herphysical traits. One of the traits is a finger's blood vessel pattern.

As disclosed in Jpn. Pat. Laid-open Publication No. 2003-331272, thereis an authentication apparatus that includes a turntable 7. An imagepickup camera 2 and a light source 1 are placed on either side of theturntable 7. A user puts his/her finger 3 on the center of the turntable7. The authentication apparatus spins the turntable 7 at constant speedto rotate the image pickup camera 2 and the light source 1 around thefinger 3 and takes moving images. The authentication apparatus combinesthose images to grasp all the sides of the finger. The authenticationapparatus can therefore identify a user by checking the combined image,especially around the joints of the finger.

SUMMARY OF THE INVENTION

However, since the authentication apparatus takes pictures of bloodvessels inside a finger (see FIG. 5A, for example), it is very difficultto distinguish the joints from the other parts of the finger. Sometimesthe joints may not appear on the image.

Accordingly, it may not authenticate a user precisely when theverification is done based on the finger' joints.

The present invention has been made in view of the above points and isintended to provide a verification apparatus, verification method andprogram that can precisely identify a user.

In one aspect of the present invention, a verification apparatusincludes: a detection section that detects some or all of junctionpoints, endpoints and turning points of a physical trait of a body parton an input image as feature points, the physical trait being used forverification; and a search section that searches a registered image ofthe physical trait for a pattern that is the same as or similar to apattern of the feature points in a center area of the image whosevertical center line is perpendicular to the direction of motion of thephysical trait that horizontally moves on a surface on which the bodypart is put, the registered image being taken along a curved surface ofthe body part and the center area being between two lines each of whichis a predetermined distance away from the center line in oppositedirections.

In that manner, the verification apparatus uses the feature points thatalways appear on even obscure images when it searches the registeredimage of the physical trait for a pattern that is the same as or similarto that of the input image. The verification apparatus can thereforeverify a user more precisely than when checking the joints of a finger.

In addition, while a typical verification device takes a picture of aphysical trait from one angle, this verification apparatus takes imagesof a physical trait from different angles. Accordingly, a user setshis/her body part more freely.

In another aspect of the present invention, a verification methodincludes: a first step of detecting some or all of junction points,endpoints and turning points of a physical trait of a body part on aninput image as feature points, the physical trait being used forverification; and a second step of searching a registered image of thephysical trait for a pattern that is the same as or similar to a patternof the feature points in a center area of the image whose verticalcenter line is perpendicular to the direction of motion of the physicaltrait that horizontally moves on a surface on which the body part isput, the registered image being taken along a curved surface of the bodypart and the center area being between two lines each of which is apredetermined distance away from the center line in opposite directions.

In that manner, the verification method uses the feature points thatalways appear on even obscure images when it searches the registeredimage of the physical trait for a pattern that is the same as or similarto that of the input image. The verification method can therefore verifya user more precisely than when checking the joints of a finger.

In addition, while a typical verification method takes a picture of aphysical trait from one angle, this verification method takes images ofa physical trait from different angles. Accordingly, a user sets his/herbody part more freely.

In another aspect of the present invention, a program for causing acomputer to execute: a step of detecting some or all of junction points,endpoints and turning points of a physical trait of a body part on aninput image as feature points, the physical trait being used forverification; and a step of searching a registered image of the physicaltrait for a pattern that is the same as or similar to a pattern of thefeature points in a center area of the image whose vertical center lineis perpendicular to the direction of motion of the physical trait thathorizontally moves on a surface on which the body part is put, theregistered image being taken along a curved surface of the body part andthe center area being between two lines each of which is a predetermineddistance away from the center line in opposite directions.

In that manner, the program uses the feature points that always appearon even obscure images when it searches the registered image of thephysical trait for a pattern that is the same as or similar to that ofthe input image. The program can therefore verify a user more preciselythan when checking the joints of a finger.

In addition, while a typical program takes a picture of a physical traitfrom one angle, this program takes images of a physical trait fromdifferent angles. Accordingly, a user sets his/her body part morefreely.

As mentioned above, the verification apparatus, the verification methodand the program thereof use the feature points that always appear oneven obscure images when they search the registered image of thephysical trait for a pattern that is the same as or similar to that ofthe input image. The verification apparatus, the verification method andthe program thereof can therefore verify a user more precisely than whenchecking the joints of a finger.

The nature, principle and utility of the invention will become moreapparent from the following detailed description when read inconjunction with the accompanying drawings in which like parts aredesignated by like reference numerals or characters.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings:

FIG. 1 is a block diagram illustrating the overall configuration of anauthentication apparatus according to an embodiment of the presentinvention;

FIGS. 2A to 2E are schematic diagrams illustrating a finger that isrolling on a surface;

FIG. 3 is a schematic diagram illustrating image-pickup surfaces andblood vessels' images;

FIG. 4 is a block diagram illustrating the functional configuration of acontrol section (Blood vessel registration mode);

FIGS. 5A and 5B are schematic diagrams illustrating images before andafter embossment;

FIG. 6 is a schematic diagram illustrating extracted patterns;

FIG. 7 is a schematic diagram illustrating feature points detected;

FIGS. 8A and 8B are schematic diagrams illustrating the process ofpositional difference calculation;

FIG. 9 is a schematic diagram illustrating a search area;

FIG. 10 is a schematic diagram illustrating positional differences;

FIG. 11 is a schematic diagram illustrating the process of changing theposition of a search area;

FIG. 12 is a schematic diagram illustrating the brightness of bloodvessels after an embossment process;

FIG. 13 is a schematic diagram illustrating the change of brightnessduring a pattern extraction process;

FIGS. 14A and 14B are schematic diagrams illustrating how to average thebrightness;

FIG. 15 is a schematic diagram illustrating how to clip a piece from animage;

FIG. 16 is a schematic diagram illustrating a difference between adistance that a center part traveled and a distance that an end pointtraveled on an image-pickup surface;

FIGS. 17A to 17C are schematic diagrams illustrating the effect ofdistortion correction;

FIG. 18 is a schematic diagram illustrating an attachment process;

FIGS. 19A and 19B are schematic diagrams illustrating how to generate animage to be registered;

FIG. 20 is a flowchart illustrating a registration process;

FIG. 21 is a block diagram illustrating the functional configuration ofa control section (Authentication mode);

FIG. 22 is a schematic diagram illustrating how to find out and clip averification-target area;

FIGS. 23A to 23C are schematic diagrams illustrating a correctionprocess;

FIG. 24 is a flowchart illustrating an authentication process; and

FIG. 25 is a schematic diagram illustrating how to change the shape of asearch area.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

An embodiment of the present invention will be described in detail withreference to the accompanying drawings.

(1) Overall Configuration of an Authentication Apparatus

FIG. 1 illustrates the overall configuration of an authenticationapparatus 1 according to an embodiment of the present invention. Theauthentication apparatus 1 includes a control section 10 that isconnected to an operation section 11, an image pickup section 12, amemory 13, an interface 14 and a notification section 15 via a bus 16.

The control section 10 is a microcomputer including a central processingunit (CPU), which takes overall control of the apparatus 1, a read onlymemory (ROM), which stores various programs and setting information, anda random access memory (RAM), which serves as a work memory for the CPU.

When operated by a user, the operation section 11 supplies a commandCOM1, COM2 or the like to the control section 10: The command COM1orders the control section 10 to operate in blood vessel registrationmode for registering a user's blood vessel pattern while the commandCOM2 orders the control section 10 to operate in authentication mode foridentifying a user.

When receiving the command COM1 or COM2, the control section 10 operatesin the blood vessel registration mode or the authentication mode. Inthis case, the control section 10 executes a corresponding program tocontrol the image pickup section 12, the memory 13, the interface 14 andthe notification section 15.

Based on an exposure value (EV) set by the control section 10, the imagepickup section 12 adjusts a position of an optical lens of an opticalsystem, aperture and a shutter speed (exposure time) of an image pickupelement.

The image pickup section 12 controls the image pickup element to takeimages. The image pickup element sequentially outputs image signals atpredetermined intervals. The image pickup section 12 performs ananalog-to-digital (A/D) conversion process to convert the image signalsinto digital image data and then supplies the image data to the controlsection 10.

In addition, during a period of time specified by the control section10, the image pickup section 12 drives an near infrared ray source toemit a near infrared ray to a predetermined position (also referred toas a “shooting position”) where a shooting object is placed. The nearinfrared ray is particularly absorbed in blood vessels.

When a body part is placed at the shooting position, the emitted nearinfrared ray gets into the body part. After passing through the opticalsystem and lens, the near infrared ray is received by the image pickupelement, representing a blood vessel pattern of the body part. As aresult, an image of the blood vessel pattern is formed on an imagepickup surface of the image pickup element. In that manner, the imagepickup section 12 obtains an image representing the blood vesselspattern.

The memory 13 is for example a flash memory. The memory 13 stores dataspecified by the control section 10. The control section 10 can read outthe data from the memory 13.

The interface 14 exchanges data with external devices via apredetermined transmission line.

The notification section 15 includes: a display section 15 a, whichdisplays characters, symbols and the like based on data supplied fromthe control section 10; and an audio output section 15 b, which outputssound from a speaker based on data supplied from the control section 10.

(2) Blood Vessel Registration Mode

Following describes how the apparatus operates in the blood vesselregistration mode. When receiving the command COM1, the control section10 enters the blood vessel registration mode. The notification section15 informs a user that he/she should put his/her finger on the shootingposition such that the finger pad touches its surface and then rollhis/her finger on the surface. In addition, the control section 10begins operating the image pickup section 12.

For example, when the user rolls his/her finger on the surface at theshooting position as shown in FIGS. 2A to 2E, the image pickup section12 takes pictures of the blood vessels inside the finger from differentangles as show in FIG. 3.

In this embodiment, the apparatus does not have any equipment to fix theposition of a finger. Accordingly, when the apparatus takes pictures ofthe finger's blood vessels, a position where the user puts and rollshis/her finger may differ for each time. In addition, the distance fromthe surface to the blood vessels may vary depending on how hard the userpresses his/her finger against the surface, its rotational axis and thelike.

The control section 10 receives from the image pickup section 12 (imagepickup element) the image data or the pictures of the finger ofdifferent angles. The control section 10 combines those pictures as oneimage. In addition, the control section 10 extracts feature points ofthe blood vessels from the image. The control section 10 subsequentlystores or registers the image and the extracted feature points in thememory 13 as registration data.

In that manner, the control section 10 operates in the blood vesselregistration mode.

Following describes how the control section 10 obtains the image dataand produces the registration data. Assume that the control section 10has functional components as shown in FIG. 4, including an embossmentsection 21, a pattern extraction section 22, a feature point detectionsection 23, a positional difference calculation section 24 and an imagecombination processing section 25.

The embossment section 21 receives a stream of images of the finger ofdifferent angles (i.e. image data) from the image pickup section 12.Before being received by the embossment section 21, the stream of imagesmay be thinned out at certain intervals (This process is also referredto as a “thin-out process”).

(2-1) Embossment Process

The embossment section 21 embosses a pattern of blood vessels on theimages. The embossment section 21, which receives image data D10,processes the image data D10 (such as a differential filtering processknown as Gaussian) to emboss a pattern of blood vessels on the images.The embossment section 21 subsequently supplies to the patternextraction section 22 and the positional difference calculation section24 image data D11 of embossed blood vessel pattern images.

FIGS. 5A and 5B show images before and after embossment. As shown inFIG. 5A, a non-embossed image has obscure outlines of the blood vesselpatterns. On the other hand, as shown in FIG. 5B, an embossed image hasclear outlines of the blood vessel patterns. The embossment process ofthe embossment section 21 emphasizes the outlines of blood vessels.Accordingly, the blood vessel patterns can be distinguished from theremaining part of the image.

(2-2) Pattern Extraction Process

The pattern extraction section 22 extracts a pattern of blood vessels onthe image as a pattern of lines. In this embodiment, the patternextraction section 22, which receives the image data D11, binarizes theimage data D11 and then extracts from the binarized image data a centerof the width of the blood vessels and a brightness peak of the width toobtain a pattern of lines (also referred to as a “blood vessel line”).The pattern extraction section 22 subsequently supplies to the featurepoint detection section 23 image data D12 of blood vessel lines.

As shown in FIG. 6, the pattern extraction process of the patternextraction section 22 simplifies the blood vessel patterns, expressingthem by lines.

(2-3) Feature Points Detection Process

The feature point detection section 23 detects some of the junctionpoints, endpoints, and turning points of the lines (or the line-shapedpattern) as feature points. In this embodiment, the feature pointdetection section 23, which receives the image data D12, detects fromthe image data D12 some of the end points, junction points and turningpoints of the lines as feature points, as shown in FIG. 7; In fact, thefeature point detection section 23 first connects those points (endpoints, junction points and turning points) with lines, calculates thearea of each cell surrounded by the lines, only chooses the cells whosesize is less than a predetermined threshold, and then regards the pointsof those chosen cells as feature points. This method is for exampledisclosed in Jpn. Pat. Laid-open Publication No. 2006-207033.

After detecting the feature points (end points, junction points andturning points), the feature point detection section 23 generatesposition data D13 representing the positions of the detected featurepoints and then supplies the position data D13 to the positionaldifference calculation section 24. In addition, the feature pointdetection section 23 associates the position data D13 with thecorresponding image data D12 (from which the position data D13 isproduced) and then supplies them to the image combination processingsection 25.

(2-4) Positional Difference Calculation Process

The positional difference calculation section 24 calculates a positionaldifference about a target first image (i.e. the image data D11), whichthe apparatus will process, and a previous-target second image (i.e. theimage data D11), which the apparatus has processed before the firstimage: The positional difference calculation section 24 calculates apositional difference between a blood vessel line of the first image anda corresponding blood vessel line of the second image. The first imageis also referred to as a “current image”, while the second image, whichhas been processed before the current image, is also referred to as a“previous image”.

The calculation method of the positional difference calculation section24 is for example based on the so-called optical flow. For example, asshown in FIG. 8A, the positional difference calculation section 24selects from the current image IM1 a certain point as an attention pointAP and sets a m×n pixels block (also referred to as an “attentionblock”) ABL around the attention point AP. The positional differencecalculation section 24 calculates the brightness of the attention blockABL.

As shown in FIG. 8B, the positional difference calculation section 24finds out, from the previous image IM2, a block RBL whose brightnessvalue is the nearest to that of the attention block ABL and then regardsthe center of the block RBL as a corresponding point XP for theattention point AP. The positional difference calculation section 24calculates a position vector V (Vx, Vy) drawn from a position AP′ (thisposition AP′ of the previous image IM2 is the same as the attentionpoint AP of the current image IM1) pointing to the corresponding pointXP.

The positional difference calculation section 24 repeatedly performs theabove process for a plurality of attention blocks of the current imageIM1; each time it sets an attention block ABL, the positional differencecalculation section 24 finds out a corresponding block RBL of theprevious image IM2 and calculates a position vector drawn from a pointAP′ (whose position is the same as the center of the attention block)pointing to the center of the block RBL. The positional differencecalculation section 24 subsequently calculates the average of thecalculated position vectors (i.e. the average of the horizontal vectorcomponents Vx and vertical vector components Vy) as positionaldifference and then supplies it to the image combination processingsection 25 as positional difference data D15.

In this embodiment, based on the position data D13, the positionaldifference calculation section 24 is designed to detect the featurepoints of blood vessel lines from the current image IM1 and specifythose feature points as attention points AP. That is, the attentionpoints AP are detected only from the blood vessel lines, not from allthe pixels of the image IM1. This reduces the processing load of thepositional difference calculation section 24 searching for blocks RBL.

Before searching for a block RBL whose brightness value is the nearestto that of the attention block ABL, the positional differencecalculation section 24 sets a search area SAR around the position AP′ asshown in FIG. 9. The size of the search area SAR is a certain number ofattention blocks combined. The positional difference calculation section24 searches the search area SAR for a corresponding point. In thismanner, the positional difference calculation section 24 only searchespart of the previous image IM2, not the whole image IM2. This reducesthe processing load of the positional difference calculation section 24searching for blocks RBL.

FIG. 10 illustrates the positional differences between the current imageand the previous image: Vertical lines are the blood vessel linesextracted by the pattern extraction section 22 from the current image;dots or points on the blood vessel lines are detected by the featurepoint detection section 23; and broken lines represent the blood vessellines extracted by the pattern extraction section 22 from the previousimage.

In this case, the positional differences are represented by horizontallines between the vertical lines and the broken lines. Actually, thepositional differences are calculated by averaging the position vectorsor the horizontal lines.

It is evident from FIG. 10 that the calculated positional differencesreflect the vertical difference between the current position of thefinger and the previous position (which depends on how hard the userpresses his/her finger against the surface or the like), as well as thehorizontal difference between the current and previous positions.

In addition, according to the calculated positional differences, thepositional difference calculation section 24 moves the search area SARon the previous image IM2.

The following is one of examples of moving the search area SAR. Whensearching the previous image IM2 for a point corresponding to a featurepoint (i.e. an attention point AP) that exists on a blood vessel line ofthe current image IM1, the positional difference calculation section 24uses a positional difference (VX_(−AVE), VY_(−AVE)), which had beencalculated when the previous image IM2 was regarded as a “current” imageIM1. When this positional difference (VX_(−AVE), VY_(−AVE)) is zero(which means that there is no positional difference), the positionaldifference calculation section 24 sets, on the previous image IM2, asearch area SAR1 around a position AP′, which is the same location asthe attention point AP (as shown in FIG. 11).

By the way, the positional difference (VX_(−AVE), Vy_(−AVE)), which hadbeen calculated when the previous image IM2 was regarded as a “current”image IM1, is equivalent to a positional difference between a (k−1)thimage and a (k−2)th image if a current image IM1, a previous image IM2and a more previous image are a kth image, a (k−1)th image and a (k−2)thimage respectively.

Whereas when the positional difference (VX_(−AVE), VY_(−AVE)) is notzero (which means that there is some positional difference), thepositional difference calculation section 24 moves the point AP′ by anamount equal to the positional difference and sets a search area SAR2around it (i.e. a point AP″ in FIG. 11).

In that manner, the positional difference calculation section 24 isdesigned to move the center of a search area SAR according to thepreviously-calculated positional difference (Vx_(−AVE), Vy_(−AVE)).Accordingly, the position of the search area SAR on the previous imageIM2 can be changed.

That means that the positional difference calculation section 24 movesthe search area SAR to compensate for the vertical difference betweenthe current position of the finger and the previous position (whichdepends on how hard the user presses his/her finger against the surfaceor the like), as well as the horizontal difference between the currentand previous positions.

By the way, in this embodiment, the calculation of positionaldifferences is based on the image data D11, the data produced before theprocess of the pattern extraction section 22.

This image data D11 clearly indicates a boundary between a blood vesselline and the other parts, as shown in FIGS. 5A and 5B. In addition, thebrightness of the blood vessel lines on the image is regarded asinformation representing an actual three-dimensional (cross-sectional)shape of the blood vessels, as shown in FIG. 12. However, the image dataD12 or D13 that has gone through the extraction process of the patternextraction section 22 (binarization and thinning processes (FIG. 13))presents rounded shapes of the blood vessels, as shown in FIGS. 14A and14B.

If the image data D12 or D13 are used for the process of searching aprevious image IM2 for a block whose brightness is the nearest to thatof an attention block ABL of a current image IM1 (as shown in FIG. 8B),it is difficult for the positional difference calculation section 24 tofind out an appropriate one because there may be many blocks ofsubstantially the same brightness as the attention block ABL. In thiscase, the positional difference calculation section 24 may not be ableto calculate a positional difference precisely.

That is why the positional difference calculation section 24 uses theimage data D11 (which is data generated before the extraction process ofthe pattern extraction section 22) to calculate the positionaldifferences.

On the other hand, an attention point AP of an attention block ABL of acurrent image IM1 is equivalent to a feature point detected from theimage data D13 that has gone through the extraction process of thepattern extraction section 22.

The feature points represent a pattern of blood vessel lines. A featurepoint detected from an image that has not passed through the extractionprocess of the pattern extraction section 22 may not coincide with theone detected from the image data that has gone through the extractionprocess of the pattern extraction section 22. Accordingly, this kind ofmethod (detecting a feature point from an image that has not passedthrough the extraction process) is not reliable.

Therefore, the positional difference calculation section 24 detects afeature point from the image data D13 that has passed through thepattern extraction section 22 and regards it as an attention point AP ofan attention block ABL of a current image IM1.

(2-5) Image Clipping Process

An image clipping section 25A of the image combination processingsection 25 cuts out from the input image (i.e. the image data D12) aneffective area AR extending from a line LN1 to a line N2, each of whichis a certain distance apart from a center line CLN that divides theimage into equal two pieces in the direction of motion of the shootingobject, as shown in FIG. 15.

A distance (represented by the number of pixels) DS between the centerline CLN and one of the lines LN1 and LN2 is determined such that theratio of a first motion distance to a second motion distance is lessthan one pixel: The first motion distance represents a distance that theshooting object has moved in the direction of motion with respect to thecenter of the image pickup surface while the second motion distanceindicates a distance that shooting object has moved in the direction ofmotion with respect to a line beyond the center line.

Assume that, as shown in FIG. 16, a finger, which has blood vesselsextending from the finger's center o by a distance r, is rotated aroundthe center o in a rotation direction d by an angle of Δθ. In this case,a point of a blood vessel on a cross-sectional plane that passes throughthe center o and perpendicularly crosses the image pickup surface isreferred to as Po. When the finger is rotated in the rotation directiond by an angle of Δθ the point Po moves to a position P′o. At the sametime, a point P1, which is in the rotation direction d making an angleof θ to the point Po, moves to a point P′1.

On the other hand, a distance Δxo between the points Po and P′o and adistance Δx1 between the points P1 and P′1 on the image pickup surfaceare substantially the same if the shooting object (finger) has a flatsurface. However, since the surface of the shooting object is curved,the distances Δxo and Δx1 are different from each other.

If the rotation speed is substantially slow, the distance Δxo isrepresented as follows:

$\begin{matrix}\begin{matrix}{{\Delta \; x_{0}} = {r\; {\sin ({\Delta\theta})}}} \\{\cong {{r \cdot \Delta}\; \theta}}\end{matrix} & (1)\end{matrix}$

Similarly, the distance Δx1 is represented as follows:

$\begin{matrix}\begin{matrix}{{\Delta \; x_{1}} = {{r\; {\sin \left( {\theta + {\Delta \; \theta}} \right)}} - {r\; {\sin (\theta)}}}} \\{= {r\left\{ {{{\sin (\theta)}{\cos \left( {\Delta \; \theta} \right)}} + {{\cos (\theta)}{\sin \left( {\Delta \; \theta} \right)}} - {\sin (\theta)}} \right\}}} \\{\cong {r\left\{ {{{\sin (\theta)} \cdot 1} + {{{\cos (\theta)} \cdot \Delta}\; \theta} - {\sin (\theta)}} \right\}}} \\{= {{r \cdot \Delta}\; {\theta \cdot \cos}\; (\theta)}}\end{matrix} & (2)\end{matrix}$

Based on the above equations (1) and (2), the ratio of Δxo to Δx1 is:

$\begin{matrix}{\frac{\Delta \; x_{1}}{\Delta \; x_{0}} = {\frac{{r \cdot \Delta}\; {\theta \cdot {\cos (\theta)}}}{{r \cdot \Delta}\; \theta} = {\cos (\theta)}}} & (3)\end{matrix}$

On the other hand, the angle θ between the points Po and P1 on the imagepickup surface is represented as follows:

$\begin{matrix}{\theta = {\arctan\left( \frac{r\; {\sin (\theta)}}{r} \right)}} & (4)\end{matrix}$

Accordingly, replacing “θ” in the equation (3) with the equation (4)presents the ratio of Δxo to Δx1 in the following manner:

$\begin{matrix}{\frac{\Delta \; x_{1}}{\Delta \; x_{0}} = {\cos\left( {\arctan\left( \frac{r\; {\sin (\theta)}}{r} \right)} \right)}} & (5)\end{matrix}$

Assume that the difference between Δxo and Δx1, an area (r sin(θ))between the points Po and P1 on the image pickup surface, is allowed upto ten pixels. In this case, if the width of a direction correspondingto the motion direction on the image pickup surface is the same sixtypixels as the width of a finger, the distance r from the center o isabout thirty pixels.

Accordingly, based on the equation (5), the ratio of Δxo to Δx1 is0.9388 . . . . That is, the difference between the motion distance Δxoof the point Po and the motion distance Δx1 of the point P1 is less thanone pixel. Therefore, they are regarded to be the same as the actualmotion distance Δθ. In this case, according to the equation (4), theangle θ (an angle Po-o-P1) is around 20 degrees.

Therefore, if an image to be clipped has the same width as a finger inpixels, the maximum size of the effective area AR will be one third ofthe width of the image to be clipped. In addition, an angle between theone end and the other end of the effective area AR with respect to thefinger's center is up to 40 degrees. This prevents projection distortionof the effective area AR, which might often happen when a curved objectis projected on a flat plane.

By the way, as shown in FIG. 10, the blood vessel lines indicated bybroken lines and the blood vessel lines represented by the solid linesare substantially the same around the center area of the image. On theother hand, they are different from each other around the rim of theimage. Accordingly, cutting out the effective area AR (FIG. 15) from theimage prevents projection distortion.

Instead of clipping the effective area AR from the input image, someapparatus might try to correct projection distortion by processing therim of the image. However, processing the rim of the image (FIG. 17A) tocorrect distortion enhances the effect of aliasing as shown in FIG. 17B.In this manner, the smooth lines of blood vessels on the image (FIG.17A) are transformed into the jagged lines as shown in FIG. 17C.

Accordingly, the image clipping section 25A presents a more reliableimage by clipping the effective area AR.

(2-6) Image Attachment Process

As shown in FIG. 18, an image attachment section 25B provides anexpansion image EXM to which images are attached. The image attachmentsection 25B arranges the images IM10 to IM16 on the expansion image EXM:The position of the upper left end of each of the images IM10 to IM16 isfor example determined according to a corresponding positionaldifference indicated by the position data D13.

In fact, the image attachment section 25 puts the first image IM10 onthe left-side area of the expansion image EXM. By the way, in thisembodiment, the image IM10 attached to the expansion image EXM includesboth the effective area AR (FIG. 15) and the rim area because the imageclipping section 25A did not cut off it.

After that, the image attachment section 25 puts the second andsubsequent images IM11 to IM16 one at a time such that each image'sreference point is positioned on a previously-attached image's referencepoint in accordance with the positional differences (i.e. the averagesof the horizontal vector components Vx and vertical vector components Vy[FIG. 8B]). As a result, part of a blood vessel line of each image isoverlapped with part of a blood vessel line of a previously-attachedimage.

By the way, the second and subsequent images IM11 to IM15, except thelast one IM16, only include the effective areas AR (FIG. 15) as a resultof the clipping process of the image clipping section 25A. The lastimage IM16 attached to the expansion image EXM includes both theeffective area AR (FIG. 15) and the rim area in a similar way to thefirst image IM10.

After attaching the last image IM16 to the expansion image EXM, theimage attachment section 25B cuts off the protruding portions of theextension image EXM (or the image CIM1 [FIG. 19A]) to produce arectangular image CIM2 for registration.

The image attachment section 25B subsequently recognizes the featurepoints of the blood vessel lines of the image CIM2 based on the positiondata D13 and then produces data to be registered (referred to as“registration data”) by combining the data of the feature points, thedata of the image CIM2 and the data that associates the feature pointsand the image CIM2.

In that manner, the control section 10 produces the registration data:The registration data includes an image on which blood vessels inside afinger are projected and data of the blood vessels' feature points.

(3) Registration Process

FIG. 20 is a flowchart illustrating a registration process for the bloodvessel registration mode. For ease of explanation, the apparatus doesnot reduce the number of images, which are sequentially supplied fromthe image pickup section 12.

When receiving the blood vessel registration mode execution commandCOM1, the control section 10 starts a registration process and proceedsto step SP1. At step SP1, the control section 10 controls thenotification section 15 to send a user a message that he/she should puthis/her finger on the shooting position and roll his/her finger on thesurface. At the same time, the control section 10 controls the imagepickup section 12 to start shooting.

When having received the image data supplied from the image pickupsection 12, the control section 10 at step SP2 embosses a pattern ofblood vessels on the image. At subsequent step SP3, the control section10 temporarily stores the embossed image in an internal buffer memory asa current image.

At subsequent step SP4, the control section 10 transforms the embossedblood vessels into thin lines. At subsequent step SP5, the controlsection 10 detects endpoints, junction points and turning points of thethin lines as feature points.

If the current image is the first image supplied from the image pickupsection 12, the control section 10 at step SP7 attaches the image ofthin lines onto the left side area of the expansion image EXM. Atsubsequent step SP8, the control section 10 starts to regard the image,which is temporarily stored in the internal buffer, as a previous imageand then returns to step SP2.

Whereas if the current image is not the first image supplied from theimage pickup section 12, the control section 10 at step SP9 calculates apositional difference between the previous and current images based onthe current image's feature points detected at steps SP4 and SP5: Boththe previous and current images are temporarily stored in the internalbuffer memory.

At step SP10, the control section 10 recognizes, from the averages ofthe horizontal vector components Vx and vertical vector components Vy(FIG. 8B) calculated at step SP9, a vector component that coincides withthe direction of motion of the finger (i.e. the average of the verticalvector components Vy (FIG. 8B) and then checks if it is greater than orequal to a predetermined threshold.

If the vector component is less than the threshold, the control section10 determines that the finger is substantially not moving. In this case,at subsequent step SP11, the control section 10 deletes the current andprevious embossed images from the internal buffer and then returns tostep SP2.

Whereas if the vector component is greater than or equal to thethreshold, the control section 10 at step SP12 deletes the previousembossed image from the internal buffer and then starts to regards thecurrent embossed images, which is stored in the internal buffer, as aprevious embossed image. The control section 10 subsequently proceeds tostep SP13.

At step SP13, the control section 10 checks if the number of clippedimages reaches a predetermined number. If not so, the control section 10at step SP14 clips an effective area AR (FIG. 15) from the currentembossed image. At subsequent step SP15, the control section 10 attachesthe current embossed image onto the expansion image EXM (FIG. 18) suchpart of each blood vessel line of the current image is overlapped with acorresponding part of the previously-attached image in accordance withthe positional difference calculated at step SP9. The control section 10subsequently returns to step SP2.

Whereas if the number of clipped images has reached the predeterminednumber, the control section 10 at step SP16 attaches, in a similar wayto that of step SP14, the current embossed image onto the expansionimage EXM (FIG. 18) and then clips from the expansion image EXM or (i.e.the combined image CIM1 [FIG. 19A]) an image CIM2 for registration. Thecontrol section 10 subsequently produces the registration data includingthe image CIM2 and the data of feature points of the blood vessel linesof the image CIM2.

The control section 10 at subsequent step SP17 registers theregistration data in the memory 13 (FIG. 1) and then ends theregistration process.

In that manner, the control section 10 operates in the blood vesselregistration mode.

(4) Authentication Mode

Following describes the authentication mode. When receiving the commandof the authentication mode, the control section 10 (FIG. 1) enters theauthentication mode. The control section 10 controls the notificationsection 15 to ask a user to put firmly his/her finger on the shootingposition. The control section 10 also starts operating the image pickupsection 12.

When the control section 10 has received image data from the imagepickup element of the image pickup section 12 as a result of shooting,the control section 10 performs the processes of embossment, patternextraction and feature point detection, which are the same as those ofthe blood vessel registration mode. In this manner, the control section10 obtains the resultant image data and the data of feature points ofthe blood vessel lines of that image.

The control section 10 checks if the user is a legitimate personregistered in the apparatus, by comparing the obtained feature pointsand image (also referred to as “reference points” and a “referenceimage” respectively) with the feature points and image of theregistration data (also referred to as “registered points” and a“registered image” respectively).

If the control section 10 determines that the user is not legitimate,the control section 10 notifies the user accordingly through the displaysection 15 a and the audio output section 15 b. Whereas if the controlsection 10 determines that the user is legitimate, the control section10 supplies data, which indicates the fact that the user is a legitimateperson, to a device through the interface 14. When having received thedata, the device performs a predetermined process, such as opening thedoor for the user or lifting restriction on an operation mode that wasprohibited from being performed.

In that manner, the control section 10 operates in the authenticationmode.

Following describes how the control section 10 obtains the referencepoints and reference image and how to determine the user is legitimate.Those processes can be represented by the functional blocks as shown inFIG. 21: an image clipping section 31, an image correction section 32and a verification section 33.

The image clipping section 31 receives the reference image data andreference point data as a result of the processes of embossment, patternextraction and feature point detection, which are the same as those ofthe blood vessel registration mode.

(4-1) Image Clipping Process

The image clipping section 31 searches the registered image for a partappropriate for being compared with the reference image. Specifically,as shown in FIG. 22, the image clipping section 31 recognizes a patternof reference points in an effective area AR_(R) of the reference imageIM_(R) and finds out an area of the registered image including a patternof registered points that is substantially the same as or similar to therecognized pattern. This effective area AR_(R) is the same size as thatof the blood vessel registration mode.

That prevents projection distortion, which might often happen when acurved object is projected on a flat plane. Accordingly, the imageclipping section 31 can precisely find out an area of the registeredimage including a pattern of registered points that is substantially thesame as or similar to the recognized pattern (also referred to as a“search-target pattern”).

If that pattern of registered points (which is substantially the same asor similar to the search-tarqet pattern) is not detected, then thismeans that the blood vessel lines of the reference image IM_(R) aredifferent from those of the registered image CIM2. In this case, theapparatus notifies the user of the fact that the user is not legitimate,through the display section 15 a and the audio output section 15 b.

If that pattern is detected, the image clipping section 31 clips averification-target image IM_(E), which is the same size as thereference image IM_(R) and is used for verification, from the registeredimage based on a vertical line CLN_(E) that passes through a midpointbetween left- and right-end registered points.

(4-2) Image Correction Process

The image correction section 32 corrects the side areas (end areas)AR_(E-1) and AR_(E-2) of the verification-target image IM_(E) (FIG. 22)such that the blood vessel lines of the side areas AR_(E-1) and AR_(E-2)become curved. By the way, between the side areas AR_(E-1) and AR_(E-2)is the effective area AR_(E).

In this case, the registered image CIM2 [FIG. 19B]) is a collection ofimages, each of which is a central part of the image (i.e. an effectivearea AR [FIG. 15]) whose projection distortion is relatively small.Accordingly, the effective area AR_(E) and side areas AR_(E-1) andAR_(E-2) of the verification-target image IM_(E), which was clipped fromthe registered image CIM2, has a low projection distortion.

On the other hand, side areas AR_(R-1) and AR_(R-2) of the referenceimage IM_(R) have a relatively large projection distortion (Between theside areas AR_(R-1) and AR_(R-2) is the effective area AR_(R)).Accordingly, comparing the unadjusted verification-target image IM_(E)with the reference image IM_(R) may cause an improper result (such asfailing to verify the user) due to the distinct difference between theside areas AR_(E-1) and AR_(R-1) and the side areas AR_(E-2) andAR_(R-2) in shape of blood vessel lines.

Accordingly, the image correction section 32 corrects theverification-target image IM_(E) as shown in FIGS. 23A to 23C: theinclined side areas AR_(E-1) and AR_(E-2) are projected on a plane FMsuch that they are on the same level as the effective area AR_(E), asshown in FIGS. 23A and 23B. In this case, the corrected side areasAR_(E-1) and AR_(E-2) are indicated by dotted-hatching in FIG. 23A andsolid-line arrows in FIGS. 23B and 23C. The side areas AR_(E-1) andAR_(E-2), which were inclined backwardly from the endpoints P₁ and P₁′of the effective area AR_(E), made an angle of θ_(E) (FIGS. 23B and23C).

By the way, in this case, an enneahedron in FIGS. 23A to 23C representsa finger. The width of the verification-target image IM_(E) is 60 pixelswhile the width of the effective area AR is 20 pixels (which means adistance from one end of the effective area AR to the vertical lineCLN_(E) is 10 pixels).

In this case, according to the equation (4), an angle P₀-o-P₁, or θ, isabout 20 degrees. Accordingly, an angle P₁-o-P_(x), or θ_(E), is about40 degrees. In addition, the side areas AR_(E-1) and AR_(E-2) make anangle of around 40 degrees (θ_(E)) with respect to the effective areaAR_(E).

Accordingly, the width of the projected blood vessel lines of the sidearea (indicated by dotted-hatching in FIG. 23A and solid-line arrows inFIGS. 23B and 23C) is calculated as follows: cos(40 degrees)×(the widthof the inclined side area AR_(E-1) (or AR_(E-2))).

By the way, the reason that the apparatus does not correct the sideareas AR_(R-1) and AR_(R-2) of the reference image IM_(R) is the same asthat of FIG. 18.

(4-3) Verification Process

The verification section 33 is designed to compare theverification-target image IM_(E) with the reference image IM_(R) inaccordance with the cross correlation function. As a result, theverification section 33 obtains a cross correlation value. If the crosscorrelation value is greater than or equal to a predetermined threshold,the verification section 33 determines that the user is a legitimateperson registered in the apparatus. Whereas if the cross correlationvalue is less than the threshold, the verification section 33 determinesthat the user is not legitimate.

In that manner, the control section 10 first obtains the registeredimage CIM2 by projecting the blood vessel pattern in the finger on theflat surface. The control section 10 then searches for theverification-target image IM_(E) based on the feature points of theregistered image CIM2 and reference image IM_(R) and then checks if theuser is legitimate or not by comparing the verification-target imageIM_(E) and the reference image IM_(R).

(5) Authentication Process

FIG. 24 is a flowchart illustrating an authentication process for theauthentication mode.

When having received the authentication mode execution command COM2, thecontrol section 10 starts an authentication process and then proceeds tostep SP21. At step SP21, the control section 10 controls thenotification section 15 to inform a user that he/she should put his/herfinger on the shooting position such that the finger pad touches itssurface and then roll his/her finger on the surface. In addition, thecontrol section 10 controls the image pickup section 12 to startshooting.

When having received image data (data of reference images) from theimage pickup section 12, the control section 10 at step SP22 performs,in the same way as the blood vessel registration mode, the processes ofembossment, pattern extraction and feature point detection beforeperforming a verification process.

At subsequent step SP23, the control section 10 recognizes a pattern ofreference points of the effective area AR_(R) of the reference imageIM_(R) as a search-target pattern. The control section 10 subsequentlysearches the registered image CIM2 for a pattern that is the same as orsimilar to the search-target pattern (FIG. 22).

If the control section 10 has successfully found a pattern of registeredpoints that is the same as or similar to the search-target pattern, thecontrol section 10 at step SP24 clips a verification-target imageIM_(E), which is the same size as the reference image IM_(R) and is usedfor verification, from the registered image based on a vertical lineCLN_(E) that passes through a midpoint between left- and right-endregistered points (FIG. 22). The control section 10 subsequentlyproceeds to step SP25.

At step SP25, the control section 10 corrects the verification-targetimage IM_(E) as shown in FIGS. 23A to 23C: the inclined side areasAR_(E-1) and AR_(E-2) are projected on a plane FM such that they are onthe same level as the effective area AR_(E).

Subsequently, the control section 10 at step SP26 compares theverification-target image IM_(E) (corrected at step SP25) with thereference image IM_(R) (obtained at step SP22) in accordance with thecross correlation function. As a result, the control section 10 obtainsa cross correlation value. At subsequent step SP27, the control section10 checks if the user is legitimate or not.

If the cross correlation value is less than the threshold, the controlsection 10 determines that the user is not legitimate and then returnsto step SP23 to retry the process.

Whereas if the cross correlation value is greater than or equal to thethreshold, the control section 10 determines that the user is legitimateand then proceeds to step SP28. At step SP28, the control section 10performs a process for legitimate users and then ends the authenticationprocess.

On the other hand, if the control section 10 at step SP23 fails to findout a pattern of registered points that is the same as or similar to thesearch-target pattern, then this means that the registered image CIM2(FIG. 19B) may not have a part whose cross correlation value with thereference image IM_(R) is greater than or equal to the threshold. Inthis case, the control section 10 determines that the user is notlegitimate. Subsequently, the control section 10 at step SP29 performs aprocess for unregistered users and then ends the authentication process.

In that manner, the control section 10 operates in the authenticationmode.

(6) Operation and Effect

The authentication apparatus 1 in authentication mode detects some orall of the junction points, endpoints and turning points of a physicaltrait (i.e. a blood vessel pattern of a finger) on the input referenceimage IM_(R) as feature points. The authentication apparatus 1 thensearches the registered image CIM2 for a pattern that is the same as orsimilar to a pattern of the detected feature points inside the effectivearea AR_(R).

In that manner, the authentication apparatus 1 uses the feature pointsthat always appear on the reference image IMR even when the image IMR isobscure. Accordingly, the authentication apparatus 1 can identify a userprecisely.

In addition, the authentication apparatus 1 is designed to take imagesof a finger from different angles, combines them, and then verifies auser based on the combined image CIM2. Therefore, a user puts his/herfinger more freely than when the apparatus only takes an image of afinger from one angle. Accordingly, the authentication apparatus 1 iseasy-to-use.

Moreover, if the width of an image to be clipped is equivalent to thewidth of a finger, the width of an effective area AR_(R) is one third ofthe width of the image.

This is the maximum size of the effective area AR_(R) that can alsoeliminate distortion, as described in FIG. 16. Accordingly, theauthentication apparatus 1 can search a relatively large area of theregistered image CIM1 for a pattern that is the same as or similar to apattern of the feature points while reducing distortion. Theauthentication apparatus 1 can therefore identify a user precisely.

According to the above configuration, the authentication apparatus 1 inauthentication mode detects some or all of the junction points,endpoints and turning points of a physical trait (i.e. a blood vesselpattern of a finger) on the input reference image IM_(R) as featurepoints. The authentication apparatus 1 then searches the registeredimage CIM2 for a pattern that is the same as or similar to a pattern ofthe detected feature points inside the effective area AR_(R). In thismanner, the authentication apparatus 1 uses the feature points thatalways appear on the reference image IMR even when the image IMR isobscure. Accordingly, the authentication apparatus 1 can identify a userprecisely.

(7) Other Embodiments

In the above-noted embodiment, a biometric trait to be verified is ablood vessel inside a body part. However, the present invention is notlimited to this. For example, nerves, fingerprints or face can be usedfor verification. In some cases, the apparatus may not perform anembossment process.

Moreover, in the above-noted embodiment, a body part to be verified is afinger. However, the present invention is not limited to this. Forexample, a palm, a toe, an arm or an eye may be used for verification.

Furthermore, in the above-noted embodiment, the embossment section 21uses a differentiation filter called Gaussian Filter. However, thepresent invention is not limited to this. The embossment section 21 canalso use other differentiation filters, such as Log Filter or ContrastFilter. The embossment section 21 may include a spatial filter or thelike before or after the differentiation filter to reduce noise.

Furthermore, in the above-noted embodiment, the feature point detectionsection 23 detects some of the junction points, endpoints and turningpoints as feature points. However, the present invention is not limitedto this. The feature point detection section 23 may detect junctionpoints, endpoints or turning points as feature points. Alternatively,the feature point detection section 23 may detect all of the junctionpoints, endpoints and turning points as feature points.

Furthermore, in the above-noted embodiment, the feature point detectionsection 23 detects feature points after a pattern extraction processmakes the blood vessels of the image a line-shaped pattern. However, thepresent invention is not limited to this. The feature point detectionsection 23 may detect feature points before processing the image of afinger or after an embossment process. The detailed description aboutdetecting feature points is for example disclosed in Jpn. Pat. Laid-openPublication No. 2006-207033. However, the apparatus may use anotherdifferentiation filtering method called Harris Corner.

Furthermore, in the above-noted embodiment, the positional differencecalculation section 24 detects from the previous image IM2 a block RBLwhose brightness is the nearest to the attention block ABL of thecurrent image IM2 and then regards a center point of the block RBL as acorresponding point XP (FIG. 9). However, the present invention is notlimited to this. The positional difference calculation section 24 mayfind out from the previous image IM2 a block RBL whose brightness is apredetermined number less than the brightness of the attention block ABLand then regards a center point of the block RBL as a correspondingpoint XP. This can detect a corresponding point XP more precisely.

Furthermore, in the above-noted embodiment, the positional differencecalculation section 24 moves the search area SAR on the previous imageIM2 (FIG. 11). However, the present invention is not limited to this.Instead, the positional difference calculation section 24 may change theshape of the search area SAR on the previous image IM2.

In this case, the positional difference calculation section 24calculates a variation between a positional difference, which wascalculated when the previous image IM2 was regarded a “current image”,and a previous positional difference: The calculated variation isrepresented by an x and y components (VX_(−AVE), VY_(−AVE)). Accordingto the calculated variation, the positional difference calculationsection 24 adjusts the default position of the search area SAR.

As shown in FIG. 25 (The parts of FIG. 25 have been denoted by the samereference numerals and symbols as the corresponding parts of FIG. 11),if the calculated variation is zero, then this means that a finger isnot moving. In this case the apparatus puts a search area SAR1 on theprevious image IM2 around the same position AP′ as AP. On the otherhand, if there is some variation regarding the y component (Vy_(−AVE)),then this means that a finger is rolling. In this case, the apparatusputs a search area SAR2 on the previous image IM2 such that its oneside, which coincides with the direction that the finger is not moving,is minimized while the other side, which coincides with the directionthat the finger is moving, is adjusted depending on how fast the fingeris moving. In this manner, the search area SAR2 can effectively cover anarea in which a finger is moving.

Furthermore, in the above-noted embodiment, the positional differencecalculation section 24 calculates a positional difference in thefollowing manner: the positional difference calculation section 24detects a corresponding point XP on the previous image IM2, based oneach point AP′ or feature point detected from the current image IM1, andthen calculates, as a positional difference, the average of the positionvector drawn from a point AP′ pointing to the corresponding point XP(i.e. the averages of the horizontal vector components Vx and verticalvector components Vy) (FIGS. 8A and 8B). However, the present inventionis not limited to this. Alternatively, the positional difference may bea value (representative value) calculated from the position vectors inaccordance with a statistical method, such as a maximum, minimum valueor standard deviation of the position vector.

Furthermore, in the above-noted embodiment, the positional differencecalculation section 24 determines a corresponding point XP based on eachposition AP′ of all the feature points detected from the current imageIM1 (FIGS. 8A and 8B). However, the present invention is not limited tothis. Alternatively, the positional difference calculation section 24may determine a corresponding point XP based on each position of thefeature points inside the effective area AR (FIG. 15) of the currentimage IM1. This reduces the effect of distortion correction, which isreflected on the positional difference calculated. Accordingly, thatallows the apparatus to calculate the positional difference moreprecisely.

Furthermore, in the above-noted embodiment, the image combinationprocessing section 25 attached the first and last images to theexpansion image EXM before clipping an effective area AR (FIG. 15) fromthem (FIG. 18). However, the present invention is not limited to this.The effective areas AR (FIG. 15), clipped from them, may be attached tothe expansion image EXM.

Furthermore, the verification method of the above-noted embodiment is tocompare the whole area of the reference image IM_(R) with acorresponding area. However the present invention is not limited tothis. Instead, the apparatus may compare only an effective area AR_(R)of the reference image IM_(R) with a corresponding area.

Furthermore, in the above-noted embodiment, the apparatus attachesimages to the expansion image EXM after extracting patterns from them.However, the present invention is not limited to this. Instead, theapparatus may attach, after an embossment process, the images to theexpansion image EXM and then extract patterns from them.

Furthermore, in the above-noted embodiment, the apparatus is designed toregister the expansion image (CIM2 [FIG. 19B]) and the data of thefeature points regarding the blood vessel lines on that image. However,the present invention is not limited to this. Alternatively, theapparatus may only register the expansion image. In this case, theapparatus in authentication mode may detect registered points as well asreference points: Accordingly, the apparatus can present the same effectas the above-noted embodiment.

Furthermore, in the above-noted embodiment, the apparatus is designed toregister the expansion image (CIM2 [FIG. 19B]) and the data of thefeature points regarding the blood vessel lines on that image. However,the present invention is not limited to this. Alternatively, theapparatus may only register the data of the feature points regarding theblood vessel lines on the expansion image (CIM2 [FIG. 19B]). This allowsthe apparatus not to perform a comparison process of comparing areference image, which is input when a user is verified, with averification-target image, which is clipped from a registered image,reducing the processing load of the apparatus.

Furthermore, in the above-noted embodiment, the apparatus operates inthe blood vessel registration mode or authentication mode by executingprograms stored in the ROM. However, the present invention is notlimited to this. Those programs may be installed in the apparatus fromprogram storage media, such as compact disc (CD), digital versatile disc(DVD) or semiconductor memories, or may be acquired from a programprovision server via the Internet.

Furthermore, in the above-noted embodiment, the registration process andthe authentication process are performed by the control section 10.However, the present invention is not limited to this. Part of thoseprocesses may be performed by a graphics work station.

Furthermore, in the above-noted embodiment, the authentication apparatus1 is equipped with the image-pickup function, the verification functionand the registration function. However, the present invention is notlimited to this. One or some of the functions may be realized by anotherapparatus.

The above-noted method can be applied to biometric verification.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

1. A verification apparatus comprising: a detection section that detectssome or all of junction points, endpoints and turning points of aphysical trait of a body part on an input image as feature points, thephysical trait being used for verification; and a search section thatsearches a registered image of the physical trait for a pattern that isthe same as or similar to a pattern of the feature points in a centerarea of the image whose vertical center line is perpendicular to thedirection of motion of the physical trait that horizontally moves on asurface on which the body part is put, the registered image being takenalong a curved surface of the body part and the center area beingbetween two lines each of which is a predetermined distance away fromthe center line in opposite directions.
 2. The verification apparatusaccording to claim 1, further comprising an embossment section thatembosses the physical trait on an input image, wherein the detectionsection detects the feature points from the embossed physical trait. 3.The verification apparatus according to claim 1, further comprising: anembossment section that embosses the physical trait on an input image;and a pattern extraction section that extracts the embossed physicaltrait as a line-shaped pattern, wherein the detection section detectsthe feature points from the line-shaped pattern.
 4. The verificationapparatus according to claim 1, wherein the distance is determined suchthat, if the body part's physical trait horizontally moves along thesurface, a ratio of a distance that the physical trait has traveled fromthe center line to a distance that the physical trait has traveled fromthe line becomes less than one pixel.
 5. The verification apparatusaccording to claim 1, wherein if the number of pixels of the width of animage to be clipped is equivalent to that of the width of a finger, thewidth of the center area is equivalent to one third of the number ofpixels of the width of the image.
 6. The verification apparatusaccording to claim 1, further comprising: a clipping section that uses,if the search section has found a pattern that is the same as or similarto a pattern of the feature points in the center area, the pattern toclip from the registered image a verification-target image to becompared with the input image; a correction section that corrects theverification-target image such that a blood vessel line on end areas ofthe verification-target image becomes curved, the end areas surroundinga central area of the verification-target image; and a calculationsection that calculates a cross correlation value between the correctedverification-target image and the input image.
 7. The verificationapparatus according to claim 6, wherein the correction section correctsthe verification-target image such that the end areas that backwardlyincline at a predetermined angle to the center area of theverification-target image are projected on the same plane as the centerarea.
 8. A verification method comprising: a first step of detectingsome or all of junction points, endpoints and turning points of aphysical trait of a body part on an input image as feature points, thephysical trait being used for verification; and a second step ofsearching a registered image of the physical trait for a pattern that isthe same as or similar to a pattern of the feature points in a centerarea of the image whose vertical center line is perpendicular to thedirection of motion of the physical trait that horizontally moves on asurface on which the body part is put, the registered image being takenalong a curved surface of the body part and the center area beingbetween two lines each of which is a predetermined distance away fromthe center line in opposite directions.
 9. A program for causing acomputer to execute: a step of detecting some or all of junction points,endpoints and turning points of a physical trait of a body part on aninput image as feature points, the physical trait being used forverification; and a step of searching a registered image of the physicaltrait for a pattern that is the same as or similar to a pattern of thefeature points in a center area of the image whose vertical center lineis perpendicular to the direction of motion of the physical trait thathorizontally moves on a surface on which the body part is put, theregistered image being taken along a curved surface of the body part andthe center area being between two lines each of which is a predetermineddistance away from the center line in opposite directions.